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针对常规超声波传感器网络传感器节点布置密度大的不足,提出了一种基于单对超声波传感器的非同步到达时间(ATOA)测距的定位方法.该方法利用了分时到达的超声波测距信息和航迹推算得到的位移矢量信息获得自主移动机器人的起始位姿信息.分析了该方法中各传感器的误差因素对计算结果的影响,并根据分析结果提出了一种圆周定位的优化方案.同时还提出了一种双层卡尔曼滤波器来融合多组定位测算结果的算法,提高了机器人定位精度,实现了机器人轨迹跟踪.计算机仿真结果表明使用圆周定位方法和双层卡尔曼滤波算法能有效地在较短时间内获得自主移动机器人较高的位姿精度.
Aiming at the shortage of node layout density of conventional ultrasonic sensor network, a method of positioning non-synchronous arrival time (ATOA) based on single-pair ultrasonic sensor is proposed. This method utilizes the ultrasonic ranging information and navigation The displacement vector information obtained from the trajectory is used to obtain the initial pose and pose information of the autonomous mobile robot.The influence of the error of each sensor in the method on the calculation results is analyzed and an optimization scheme of the circumferential positioning is proposed according to the analysis results. This paper proposes a double-layer Kalman filter to fuse the results of multi-group positioning algorithm, which improves the robot’s positioning accuracy and realizes the robot trajectory tracking. Computer simulation results show that using the circumferential positioning method and the double Kalman filter algorithm can effectively In a relatively short period of time, the autonomous mobile robot has higher pose accuracy.